/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "perf_precomp.hpp" using namespace std; using namespace testing; using namespace perf; ////////////////////////////////////////////////////////////////////// // Norm DEF_PARAM_TEST(Sz_Depth_Norm, cv::Size, MatDepth, NormType); PERF_TEST_P(Sz_Depth_Norm, Norm, Combine(CUDA_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32S, CV_32F), Values(NormType(cv::NORM_INF), NormType(cv::NORM_L1), NormType(cv::NORM_L2)))) { const cv::Size size = GET_PARAM(0); const int depth = GET_PARAM(1); const int normType = GET_PARAM(2); cv::Mat src(size, depth); if (depth == CV_8U) cv::randu(src, 0, 254); else declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::cuda::GpuMat d_buf; double gpu_dst; TEST_CYCLE() gpu_dst = cv::cuda::norm(d_src, normType, d_buf); SANITY_CHECK(gpu_dst, 1e-6, ERROR_RELATIVE); } else { double cpu_dst; TEST_CYCLE() cpu_dst = cv::norm(src, normType); SANITY_CHECK(cpu_dst, 1e-6, ERROR_RELATIVE); } } ////////////////////////////////////////////////////////////////////// // NormDiff DEF_PARAM_TEST(Sz_Norm, cv::Size, NormType); PERF_TEST_P(Sz_Norm, NormDiff, Combine(CUDA_TYPICAL_MAT_SIZES, Values(NormType(cv::NORM_INF), NormType(cv::NORM_L1), NormType(cv::NORM_L2)))) { const cv::Size size = GET_PARAM(0); const int normType = GET_PARAM(1); cv::Mat src1(size, CV_8UC1); declare.in(src1, WARMUP_RNG); cv::Mat src2(size, CV_8UC1); declare.in(src2, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src1(src1); const cv::cuda::GpuMat d_src2(src2); double gpu_dst; TEST_CYCLE() gpu_dst = cv::cuda::norm(d_src1, d_src2, normType); SANITY_CHECK(gpu_dst); } else { double cpu_dst; TEST_CYCLE() cpu_dst = cv::norm(src1, src2, normType); SANITY_CHECK(cpu_dst); } } ////////////////////////////////////////////////////////////////////// // Sum PERF_TEST_P(Sz_Depth_Cn, Sum, Combine(CUDA_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F), CUDA_CHANNELS_1_3_4)) { const cv::Size size = GET_PARAM(0); const int depth = GET_PARAM(1); const int channels = GET_PARAM(2); const int type = CV_MAKE_TYPE(depth, channels); cv::Mat src(size, type); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::Scalar gpu_dst; TEST_CYCLE() gpu_dst = cv::cuda::sum(d_src); SANITY_CHECK(gpu_dst, 1e-5, ERROR_RELATIVE); } else { cv::Scalar cpu_dst; TEST_CYCLE() cpu_dst = cv::sum(src); SANITY_CHECK(cpu_dst, 1e-6, ERROR_RELATIVE); } } ////////////////////////////////////////////////////////////////////// // SumAbs PERF_TEST_P(Sz_Depth_Cn, SumAbs, Combine(CUDA_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F), CUDA_CHANNELS_1_3_4)) { const cv::Size size = GET_PARAM(0); const int depth = GET_PARAM(1); const int channels = GET_PARAM(2); const int type = CV_MAKE_TYPE(depth, channels); cv::Mat src(size, type); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::Scalar gpu_dst; TEST_CYCLE() gpu_dst = cv::cuda::absSum(d_src); SANITY_CHECK(gpu_dst, 1e-6, ERROR_RELATIVE); } else { FAIL_NO_CPU(); } } ////////////////////////////////////////////////////////////////////// // SumSqr PERF_TEST_P(Sz_Depth_Cn, SumSqr, Combine(CUDA_TYPICAL_MAT_SIZES, Values<MatDepth>(CV_8U, CV_16U, CV_32F), CUDA_CHANNELS_1_3_4)) { const cv::Size size = GET_PARAM(0); const int depth = GET_PARAM(1); const int channels = GET_PARAM(2); const int type = CV_MAKE_TYPE(depth, channels); cv::Mat src(size, type); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::Scalar gpu_dst; TEST_CYCLE() gpu_dst = cv::cuda::sqrSum(d_src); SANITY_CHECK(gpu_dst, 1e-6, ERROR_RELATIVE); } else { FAIL_NO_CPU(); } } ////////////////////////////////////////////////////////////////////// // MinMax PERF_TEST_P(Sz_Depth, MinMax, Combine(CUDA_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F, CV_64F))) { const cv::Size size = GET_PARAM(0); const int depth = GET_PARAM(1); cv::Mat src(size, depth); if (depth == CV_8U) cv::randu(src, 0, 254); else declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); double gpu_minVal, gpu_maxVal; TEST_CYCLE() cv::cuda::minMax(d_src, &gpu_minVal, &gpu_maxVal, cv::cuda::GpuMat()); SANITY_CHECK(gpu_minVal, 1e-10); SANITY_CHECK(gpu_maxVal, 1e-10); } else { double cpu_minVal, cpu_maxVal; TEST_CYCLE() cv::minMaxLoc(src, &cpu_minVal, &cpu_maxVal); SANITY_CHECK(cpu_minVal); SANITY_CHECK(cpu_maxVal); } } ////////////////////////////////////////////////////////////////////// // MinMaxLoc PERF_TEST_P(Sz_Depth, MinMaxLoc, Combine(CUDA_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F, CV_64F))) { const cv::Size size = GET_PARAM(0); const int depth = GET_PARAM(1); cv::Mat src(size, depth); if (depth == CV_8U) cv::randu(src, 0, 254); else declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); double gpu_minVal, gpu_maxVal; cv::Point gpu_minLoc, gpu_maxLoc; TEST_CYCLE() cv::cuda::minMaxLoc(d_src, &gpu_minVal, &gpu_maxVal, &gpu_minLoc, &gpu_maxLoc); SANITY_CHECK(gpu_minVal, 1e-10); SANITY_CHECK(gpu_maxVal, 1e-10); } else { double cpu_minVal, cpu_maxVal; cv::Point cpu_minLoc, cpu_maxLoc; TEST_CYCLE() cv::minMaxLoc(src, &cpu_minVal, &cpu_maxVal, &cpu_minLoc, &cpu_maxLoc); SANITY_CHECK(cpu_minVal); SANITY_CHECK(cpu_maxVal); } } ////////////////////////////////////////////////////////////////////// // CountNonZero PERF_TEST_P(Sz_Depth, CountNonZero, Combine(CUDA_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F, CV_64F))) { const cv::Size size = GET_PARAM(0); const int depth = GET_PARAM(1); cv::Mat src(size, depth); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); int gpu_dst = 0; TEST_CYCLE() gpu_dst = cv::cuda::countNonZero(d_src); SANITY_CHECK(gpu_dst); } else { int cpu_dst = 0; TEST_CYCLE() cpu_dst = cv::countNonZero(src); SANITY_CHECK(cpu_dst); } } ////////////////////////////////////////////////////////////////////// // Reduce CV_ENUM(ReduceCode, REDUCE_SUM, REDUCE_AVG, REDUCE_MAX, REDUCE_MIN) enum {Rows = 0, Cols = 1}; CV_ENUM(ReduceDim, Rows, Cols) DEF_PARAM_TEST(Sz_Depth_Cn_Code_Dim, cv::Size, MatDepth, MatCn, ReduceCode, ReduceDim); PERF_TEST_P(Sz_Depth_Cn_Code_Dim, Reduce, Combine(CUDA_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_16S, CV_32F), Values(1, 2, 3, 4), ReduceCode::all(), ReduceDim::all())) { const cv::Size size = GET_PARAM(0); const int depth = GET_PARAM(1); const int channels = GET_PARAM(2); const int reduceOp = GET_PARAM(3); const int dim = GET_PARAM(4); const int type = CV_MAKE_TYPE(depth, channels); cv::Mat src(size, type); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::cuda::GpuMat dst; TEST_CYCLE() cv::cuda::reduce(d_src, dst, dim, reduceOp, CV_32F); CUDA_SANITY_CHECK(dst); } else { cv::Mat dst; TEST_CYCLE() cv::reduce(src, dst, dim, reduceOp, CV_32F); CPU_SANITY_CHECK(dst); } } ////////////////////////////////////////////////////////////////////// // Normalize DEF_PARAM_TEST(Sz_Depth_NormType, cv::Size, MatDepth, NormType); PERF_TEST_P(Sz_Depth_NormType, Normalize, Combine(CUDA_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F, CV_64F), Values(NormType(cv::NORM_INF), NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_MINMAX)))) { const cv::Size size = GET_PARAM(0); const int type = GET_PARAM(1); const int norm_type = GET_PARAM(2); const double alpha = 1; const double beta = 0; cv::Mat src(size, type); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::cuda::GpuMat dst; TEST_CYCLE() cv::cuda::normalize(d_src, dst, alpha, beta, norm_type, type, cv::cuda::GpuMat()); CUDA_SANITY_CHECK(dst, 1e-6); } else { cv::Mat dst; TEST_CYCLE() cv::normalize(src, dst, alpha, beta, norm_type, type); CPU_SANITY_CHECK(dst); } } ////////////////////////////////////////////////////////////////////// // MeanStdDev PERF_TEST_P(Sz, MeanStdDev, CUDA_TYPICAL_MAT_SIZES) { const cv::Size size = GetParam(); cv::Mat src(size, CV_8UC1); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::Scalar gpu_mean; cv::Scalar gpu_stddev; TEST_CYCLE() cv::cuda::meanStdDev(d_src, gpu_mean, gpu_stddev); SANITY_CHECK(gpu_mean); SANITY_CHECK(gpu_stddev); } else { cv::Scalar cpu_mean; cv::Scalar cpu_stddev; TEST_CYCLE() cv::meanStdDev(src, cpu_mean, cpu_stddev); SANITY_CHECK(cpu_mean); SANITY_CHECK(cpu_stddev); } } ////////////////////////////////////////////////////////////////////// // Integral PERF_TEST_P(Sz, Integral, CUDA_TYPICAL_MAT_SIZES) { const cv::Size size = GetParam(); cv::Mat src(size, CV_8UC1); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::cuda::GpuMat dst; TEST_CYCLE() cv::cuda::integral(d_src, dst); CUDA_SANITY_CHECK(dst); } else { cv::Mat dst; TEST_CYCLE() cv::integral(src, dst); CPU_SANITY_CHECK(dst); } } ////////////////////////////////////////////////////////////////////// // IntegralSqr PERF_TEST_P(Sz, IntegralSqr, CUDA_TYPICAL_MAT_SIZES) { const cv::Size size = GetParam(); cv::Mat src(size, CV_8UC1); declare.in(src, WARMUP_RNG); if (PERF_RUN_CUDA()) { const cv::cuda::GpuMat d_src(src); cv::cuda::GpuMat dst; TEST_CYCLE() cv::cuda::sqrIntegral(d_src, dst); CUDA_SANITY_CHECK(dst); } else { FAIL_NO_CPU(); } }